Synchronized navigation of medical images

ABSTRACT

Disclosed herein is a framework for facilitating synchronized image navigation. In accordance with one aspect, at least first and second medical images are received. A non-linear mapping between the first and second medical images is generated. A selection of a given location in the first medical image is received in response to a user&#39;s navigational operation. Without deforming the second medical image, a target location in the second medical image is determined by using the non-linear mapping. The target location corresponds to the given location in the first medical image. An optimized deformation-free view of the second medical image is generated based at least in part on the target location. While the user performs navigational operations on the first medical image, the framework repeatedly receives the selection of the given location, determines the target location using the non-linear mapping, and generates the optimized deformation-free view of the second medical image based at least in part on the target location.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods forsynchronized navigation of medical images.

BACKGROUND

The field of medical imaging has seen significant advances since thetime X-Rays were first used to determine anatomical abnormalities.Medical imaging hardware has progressed in the form of newer machinessuch as Medical Resonance Imaging (MRI) scanners, Computed AxialTomography (CAT) scanners, etc. Digital medical images are constructedusing raw image data obtained from such scanners. Digital medical imagesare typically either a two-dimensional (“2-D”) image made of pixelelements or a three-dimensional (“3-D”) image made of volume elements(“voxels”). Because of large amounts of image data generated in anygiven scan, there has been and remains a need for developing imageprocessing techniques that can automate some or all of the processes todetermine the presence of anatomical abnormalities in scanned medicalimages.

In the study and analysis of such medical images, it is often necessaryfor the radiologist to compare the current study to prior studies of thesame subject. The prior studies may be acquired by the same or ofdifferent image modality. For example, the radiologist may need tocompare and evaluate multiple studies acquired at different times usingMagnetic Resonance (MR), Computed Tomography (CT), X-Ray films (XR),Ultrasound (US), Positron Emission Tomography (PET), etc.

It is a highly challenging task to accurately relate information inimages that are acquired by different scanners at different times. Thisis because different modalities have widely different intensity andcontrast responses to the different tissue types. In addition, differentmodalities employ different image formation processes that give rise tomodality-specific spatial resolution, field of view and noisecharacteristics. Even further, some modalities (e.g., MR, CT, PET, etc.)produce a 3D volume of data, while other modalities (e.g., XR, US, etc.)produce 2D images. The task of identifying which point in one imagecorresponds to a given point in the other image is typically performedentirely manually, which is very time-consuming and error-prone.

Accordingly, there exists a need to provide an improved framework forfacilitating comparison of different images.

SUMMARY

The present disclosure relates to a framework for facilitatingsynchronized image navigation. In accordance with one aspect, at leastfirst and second medical images are received. A non-linear mappingbetween the first and second medical images is generated. A selection ofa given location in the first medical image is received in response to auser's navigational operation. Without deforming the second medicalimage, a target location in the second medical image is determined byusing the non-linear mapping. The target location corresponds to thegiven location in the first medical image. An optimized deformation-freeview of the second medical image is generated based at least in part onthe target location. While the user performs navigational operations onthe first medical image, the framework repeatedly receives the selectionof the given location, determines the target location using thenon-linear mapping, and generates the optimized deformation-free view ofthe second medical image based at least in part on the target location.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the followingdetailed description. It is not intended to identify features oressential features of the claimed subject matter, nor is it intendedthat it be used to limit the scope of the claimed subject matter.Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present disclosure and many of theattendant aspects thereof will be readily obtained as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings.Furthermore, it should be noted that the same numbers are usedthroughout the drawings to reference like elements and features.

FIG. 1 shows an exemplary computer system;

FIG. 2 shows an exemplary method of synchronized image navigation;

FIGS. 3a-b show image navigation using traditional rigid and non-rigidimage registration respectively;

FIGS. 4a-b show image navigation using one implementation of the presentframework;

FIG. 5 shows an exemplary user interface in accordance with oneimplementation; and

FIG. 6 shows another exemplary user interface in accordance with oneimplementation.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific components, devices, methods, etc., inorder to provide a thorough understanding of embodiments of the presentinvention. It will be apparent, however, to one skilled in the art thatthese specific details need not be employed to practice embodiments ofthe present invention. In other instances, well-known materials ormethods have not been described in detail in order to avoidunnecessarily obscuring embodiments of the present invention. While theinvention is susceptible to various modifications and alternative forms,specific embodiments thereof are shown by way of example in the drawingsand will herein be described in detail. It should be understood,however, that there is no intent to limit the invention to theparticular forms disclosed, but on the contrary, the invention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

The term “x-ray image” as used herein may mean a visible x-ray image(e.g., displayed on a video screen) or a digital representation of anx-ray image (e.g., a file corresponding to the pixel output of an x-raydetector). The term “in-treatment x-ray image” as used herein may referto images captured at any point in time during a treatment deliveryphase of a radiosurgery or radiotherapy procedure, which may includetimes when the radiation source is either on or off. From time to time,for convenience of description, CT imaging data may be used herein as anexemplary imaging modality. It will be appreciated, however, that datafrom any type of imaging modality including but not limited to X-Rayradiographs, MRI, CT, PET (positron emission tomography), PET-CT, SPECT,SPECT-CT, MR-PET, 3D ultrasound images or the like may also be used invarious embodiments of the invention.

Unless stated otherwise as apparent from the following discussion, itwill be appreciated that terms such as “segmenting,” “generating,”“registering,” “determining,” “aligning,” “positioning,” “processing,”“computing,” “selecting,” “estimating,” “detecting,” “tracking” or thelike may refer to the actions and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices. Embodiments of the methods described herein may be implementedusing computer software. If written in a programming language conformingto a recognized standard, sequences of instructions designed toimplement the methods can be compiled for execution on a variety ofhardware platforms and for interface to a variety of operating systems.In addition, embodiments of the present invention are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implementembodiments of the present invention.

As used herein, the term “image” refers to multi-dimensional datacomposed of discrete image elements (e.g., pixels for 2-D images andvoxels for 3-D images). The image may be, for example, a medical imageof a subject collected by computer tomography, magnetic resonanceimaging, ultrasound, or any other medical imaging system known to one ofskill in the art. The image may also be provided from non-medicalcontexts, such as, for example, remote sensing systems, electronmicroscopy, etc. Although an image can be thought of as a function froma domain to another domain, the methods of the inventions are notlimited to such images, and can be applied to images of any dimension,e.g., a 2-D picture or a 3-D volume. For a 2- or 3-dimensional image,the domain of the image is typically a 2- or 3-dimensional rectangulararray, wherein each pixel or voxel can be addressed with reference to aset of 2 or 3 mutually orthogonal axes. The terms “digital” and“digitized” as used herein will refer to images or volumes, asappropriate, in a digital or digitized format acquired via a digitalacquisition system or via conversion from an analog image.

The present disclosure describes a framework that facilitates the jointstudy of two or more medical images of a subject. The medical images maybe acquired at the same or different times, viewpoints, and/or by thesame or different modalities, including but not limited to, magneticresonance (MR) imaging, computed tomography (CT), helical CT, x-ray,positron emission tomography (PET), PET-CT, fluoroscopic, ultrasound,single-photon emission computed tomography (SPECT), SPECT-CT, MR-PET,etc. In addition, the medical images may include different number ofdimensions. For example, one image may be two-dimensional, while anotherimage is three-dimensional.

In accordance with one aspect of the present framework, multiple imagesare synchronously navigated by generating a non-linear mapping. Thenon-linear mapping is used to map a given location in the first image toa corresponding target location in the second image. Since thenon-linear mapping is merely used to synchronize the navigation of thecursor (or any type of moving pointer that indicates a position), andnot applied to deform the second image, the original shape andappearance of any structure or feature in the second image is preserved.This allows the user to easily navigate, compare and study differentimages without introducing any distortion to the original images. Theseexemplary advantages and features will be described in more detail inthe following description.

FIG. 1 shows an exemplary computer system for implementing a method andsystem of the present disclosure. The computer system referred togenerally as system 100 may include, inter alia, a central processingunit (CPU) 101, a non-transitory computer-readable media 104, a printerinterface 110, a display unit 111, a local area network (LAN) datatransmission controller 105, a LAN interface 106, a network controller103, an internal bus 102, and one or more input devices 109, forexample, a keyboard, mouse, touch screen, etc. Computer system 100 mayfurther include support circuits such as a cache, power supply, clockcircuits and a communications bus. Various other peripheral devices,such as additional data storage devices and printing devices, may alsobe connected to the computer system 100.

The present technology may be implemented in various forms of hardware,software, firmware, special purpose processors, or a combinationthereof, either as part of the microinstruction code or as part of anapplication program or software product, or a combination thereof, whichis executed via the operating system. In one implementation, thetechniques described herein may be implemented as computer-readableprogram code tangibly embodied in non-transitory computer-readable media104. In particular, the present techniques may be implemented by avisualization unit 107. Non-transitory computer-readable media 104 mayinclude random access memory (RAM), read only memory (ROM), magneticfloppy disk, flash memory, and other types of memories, or a combinationthereof. The computer-readable program code is executed by CPU 101 toprocess and display images (e.g., MR or CT images) acquired by animaging device (e.g., MR or CT scanner). As such, the computer system100 is a general-purpose computer system that becomes a specific purposecomputer system when executing the computer-readable program code. Thecomputer-readable program code is not intended to be limited to anyparticular programming language and implementation thereof. It will beappreciated that a variety of programming languages and coding thereofmay be used to implement the teachings of the disclosure containedherein.

The same or different computer-readable media 104 may be used forstoring a knowledge base, individual patient data, database ofpreviously treated patients (e.g., training data), and so forth. Thepatient records, including associated image data, may be stored inexternal storage or other memories. The external storage may beimplemented using a database management system (DBMS) managed by the CPU101 and residing on a memory, such as a hard disk, RAM, or removablemedia. The external storage may be implemented on one or more additionalcomputer systems. For example, the external storage may include a datawarehouse system residing on a separate computer system, a picturearchiving and communication system (PACS), or any other now known orlater developed hospital, medical institution, medical office, testingfacility, pharmacy or other medical patient record storage system.

It is to be understood that, because some of the constituent systemcomponents and method steps depicted in the accompanying figures can beimplemented in software, the actual connections between the systemscomponents (or the process steps) may differ depending upon the mannerin which the present invention is programmed. Given the teachings of thepresent invention provided herein, one of ordinary skill in the relatedart will be able to contemplate these and similar implementations orconfigurations of the present invention.

FIG. 2 shows an exemplary method 200 of synchronized image navigation.The steps of the method 200 may be performed in the order shown or adifferent order. Additional, different, or fewer steps may be provided.Further, the method 200 may be implemented with the system 100 of FIG.1, a different system, or a combination thereof.

As shown in FIG. 2, at 202, at least first and second medical images arereceived. The first and second medical images include a representationof at least a portion of an anatomical structure (e.g., heart, brain,bone, etc.). The first and second medical images may be acquired usingthe same modality and from the same patient at different times. This isuseful in, for example, monitoring changes in a tumor or lesion in apatient over time. In addition, the first and second medical images maybe acquired by different imaging modalities, which may cause the imagesto have different intensity and contrast characteristics, and/ordifferent numbers of dimensions. For instance, the first medical imagemay be a three-dimensional CT or MR image, while the second medicalimage may be a two-dimensional XR or US image.

The first and second medical images may be stored in Digital Imaging andCommunications in Medicine (DICOM) format. Any other digital file formatmay also be used. In addition, the first and second medical images maybe received from, for example, a storage device, a database system or anarchiving system, such as a picture archiving and communication (PACS)system. Further, the medical images may also be derived from originallyacquired image data, such as Maximum Intensity Projection (MaxIP)images, Minimum Intensity Projection (MinIP) images, filtered images,and so forth.

At 204, a non-linear mapping between the first and second medical imagesmay be generated. The non-linear mapping relates the position of afeature in the first medical image to the corresponding position insecond medical image. The mapping may be provided in the form of avector field or matrix.

In some implementations, the non-linear mapping is generated by anon-rigid registration. Registration generally refers to the process ofaligning two or more images that represent the same feature. Moreparticularly, registration refers to determining a transformation thatcan relate the position of features in the first medical image with theposition of corresponding features in the second medical image. Rigidregistrations involve linear transformations, such as rotation, scalingand other affine transforms, while non-rigid registrations allow elasticor non-linear transformations that are capable of local deformation.

The non-linear mapping may also be generated by a rigid transformationand a non-linear interpolation between landmarks. By explicitly avoidingdirect registration between the first and second images, the presentframework can advantageously handle images of the same structure thatappear very different in the different images. To estimate the rigidtransformation and non-linear interpolation, one or more first landmarksin the first medical image may be identified and matched tocorresponding second landmarks in the second medical image.

Each landmark is indicative of a predetermined position of a respectivefeature of the anatomical structure in the medical image. Exemplaryfeatures that may be used as landmarks include the center of rightand/or left kidneys, vertebrae, femur head, humerus heads, neck, top ofthe skull, base of the skull, top of lungs, aortic arch, pulmonarytrunk, and so forth. These landmarks may be automatically orsemi-automatically identified using, for example, a machinelearning-based detection technique. Alternatively, the landmarks may beinteractively defined or modified by the user. For example, the system100 may provide a user interface to receive user input (e.g.,user-placed marker or location information) identifying or modifyingcertain first and second landmarks to anchor the non-linear mapping. Theframework then automatically generates or re-generates the non-linearmapping in response to the user input. The user may also introduce othertypes of constraints to the non-linear mapping, such as defining themethod of non-linear interpolation, adding or removing landmarks,repositioning landmarks, and so forth. Such user interface facilitatesan optimal workflow that enables easy incremental introduction ofadditional constraints that the system 100 may take into account toimprove the non-linear mapping.

The rigid transformation may be estimated based on the first and secondlandmarks by using any known optimization or other solution spacesearching techniques to select a transform. After the rigidtransformation is estimated, a non-linear interpolation is used toestimate the elastic deformation field between the first and secondlandmarks. The deformation field may be used to infer correspondencethroughout the rest of the first and second medical images in a way thatis consistent with the matched first and second landmarks. Moreparticularly, the non-linear interpolation is applied to thecoefficients of the rigid transformation to generate a non-linearmapping for every location in the first medical image. Exemplaryinterpolation techniques that may be applied include, but are notlimited to, bilinear, trilinear, B-spline, Gaussian, windowed sincmethods, and so forth. The type of interpolation method may be selectedautomatically, or by the user as a constraint for determining thenon-linear mapping.

At 206, the system 100 receives a selection of a given location in thefirst medical image in response to a user's navigational operation. Theuser may perform a navigational operation via the user interface. Forexample, the user may perform a navigational operation by clicking, orotherwise selecting, the given location as a point of focus in the firstmedical image. The user may also perform a navigational operation byscrolling through a set of first medical images, such as a series ofimages (e.g., time series) of the same structure or feature. Other typesof navigational operations, such as changing the desired view of thestructure (e.g., different plane or orientation, zoom factor, etc.), mayalso be performed. The different images or views may be synchronized orlinked such that as the user scrolls through those images, the givenlocation is automatically set accordingly. The given location may beindicated by a cursor, cross-hair, or any other visual pointer.

At 208, the system 100 applies the non-linear mapping to the givenlocation in the first medical image to determine a target location inthe second medical image that corresponds to the given location. Thenon-linear mapping may be applied to the coordinates of the givenlocation in the first image to generate the coordinates of thecorresponding target location in the second image, without deforming thesecond image. Since the non-linear mapping is not applied to the voxels(or pixels) of the second medical image, the features (or abnormalities)in the second medical image are not deformed. Advantageously, theradiologist can inspect the features (or abnormalities) in theiroriginal shapes and appearances in the deformation-free second image.For instance, any lesions appearing in the second medical image will notappear deformed, thereby facilitating detection and comparison.

At 210, an optimized deformation-free view of the second medical imageis generated based on the target location. More particularly, thedeformation-free view may be optimized based at least in part on thetarget location. In some implementations, the deformation-free view isoptimized by displaying a target cursor, such as a cross-hair or anyother visual marker, at the target location in the second medical image.

The deformation-free view may also be optimized by automatically panningthe second image such that the given and target locations are at thesame position in each respective viewing window. Accordingly, while thenavigational cursor moves in the viewing window of the first image, thesecond image is panned such that target cursor follows the navigationalcursor to the same relative position in the viewing window of the secondimage. In other words, assuming that the origin of each windowcoordinate system is located at the same position (e.g., lower leftcorner) of each viewing window, the window coordinates of the given andtarget locations may be substantially similar after the panning. Thishelps to maintain the visual focus of the user during synchronizednavigation of the images. Alternatively, the second image may be pannedsuch that the target location is centered or at a pre-determinedposition in the viewing window. The first image may also be translatedsuch that the given location is centered or at a pre-determined viewingwindow position.

In some implementations, the deformation-free view may be optimized byrendering, for example, a multi-planar reconstruction (MPR) view orthree-dimensional (3D) view of the second image. The re-rendered secondimage may also be translated or panned such that the target location iscentered or at a pre-determined viewing window position. In addition, atarget cursor may be displayed at the target location in the re-renderedsecond image. The deformation-free view may further be optimized byautomatically adjusting the viewing parameters of the second image. Suchviewing parameters include, for instance, the field-of-view (or zoomfactor), orientation of the viewing angle, cropping plane (for 3D),window level, color-map, transfer function, and so forth.

Steps 206 through 210 may be repeated while the user performsnavigational operations on the first medical image. Such steps may berepeated to update the optimized deformation-free view of the secondmedical image interactively and in real-time.

To further illustrate the advantages of the present framework, FIGS.3a-b show image navigation using traditional rigid and non-rigid imageregistration respectively, and FIGS. 4a-b show image navigation usingone implementation of the present framework. For purposes ofillustration, the same feature A is captured in two images (e.g.,reference and target images) acquired by two different modalities. Sincethe underlying physics of acquisition are different for the differentmodalities, the same feature A may appear relatively different in theoriginal reference and target images. For example, feature A may have around shape in the reference image, and an oval shape in the targetimage. Further, it should be noted that the anatomical coordinate system(or patient coordinate system) 301 is used to describe the images inFIGS. 3a-b and 4a-b in the following description. The anatomicalcoordinate system 301 includes a sagittal plane that separates the Left(L) from the Right (R) and an axial plane that is parallel to the groundand separates the head (Superior or S) from the feet (Inferior or I).Other types of coordinate systems are also useful.

Referring to FIG. 3a , a traditional rigid registration is performed toalign a target image (not shown) with the reference image (302 a-b),thereby generating a registered image (304 a-b). However, since the samefeature A has relatively different shapes in the target and referenceimages, accurate mapping of corresponding cursor locations cannot beobtained by a rigid transformation. For example, the reference image 302a shows a navigational cursor located at a given location at a rightside of feature A. In the registered image 304 a, the correspondingtarget cursor is positioned outside, and not at the right side, offeature A. In another example, when the navigational cursor is locatedat the top side of the feature in reference image 302 b, thecorresponding target cursor in the registered image 304 b is locatedinside, and not at the top side, of feature A.

Referring to FIG. 3b , a traditional non-rigid image registration isperformed to provide additional degrees of freedom so as to take intoaccount the relatively different shape of feature A in the target image.Although the navigational and target cursor locations are accuratelymatched, deformation is introduced to the shape of feature A in theregistered image (304 c-d). For instance, feature A may appear oval inthe original target image, but round in the registered image (304 c-d).This does not allow the user or radiologist to assess the actualappearance of feature A as captured in the original target image.

FIG. 4a shows a first image 402 a-b and a second image 404 a-b. Thefirst image 402 a-b is the original reference image, while the secondimage 404 a-b is the original target image. A non-linear mapping isgenerated in accordance with one implementation of the presentframework, and used to map the given location in the first image 402 a-bto a target location in the second image 404 a-b, without deforming thesecond image 404 a-b.

As shown, the given and target locations in the first and second imagesare accurately matched, without introducing any deformation to theappearance of feature A in the second image. For instance, in the firstimage 402 a, the navigational cursor is located at the given location atthe right side of feature A. The corresponding target cursor in thesecond image 404 a is also at the right side of feature A. In addition,feature A is not deformed by the non-linear mapping. In another example,in the first image 402 b, the navigational cursor is located at the topside of feature A. The second image 404 b shows a corresponding targetcursor also at the top side of feature A.

FIG. 4b illustrates the translation of the second image 404 c-d to helpmaintain the visual focus of the user. For instance, the second image404 c may be translated to the left so that the navigational and targetcursors are at the same position (or share common window coordinates) ineach respective viewing window. In another example, the second image 404d is translated downwards such that the navigational and target cursorsare at the same window position.

FIG. 5 shows an exemplary user interface 502 in accordance with oneimplementation. The user interface 502 displays a set of first medicalimage (or reference image) 402 e and second medical image 404 e of ananatomical structure 514. It should be appreciated that more than twomedical images may also be displayed and synchronously navigated. Thefirst and second medical images (402 e and 404 e) may be acquired atdifferent viewpoints, different times and/or by different imagingmodalities. As shown, the first medical image 402 e may show a coronalview of a CT scan of the subject's chest 514, while the second medicalimage 404 e may be an axial view of a CT scan of the subject's chest 514acquired at a different time.

Synchronized navigation of the images (402 e and 404 e) may beperformed. The user may choose to perform a navigational operation bymoving either the cross-hair 516 in the first image 402 e or thecross-hair 504 in the second image 404 e. For example, when the usermoves the cross-hair 516 in the first image 402 e, the cross-hair 504 inthe second image 404 e may be automatically moved to the correspondingtarget location. Alternatively, the cross-hair 504 may be in a fixedimage position, while the second image 404 e is translated or panned.The target location of the cross-hair 504 may be automatically obtainedby applying the non-linear mapping generated by the method 200, aspreviously described with reference to FIG. 2.

The non-linear mapping may be generated automatically or on-demand inresponse to the user selecting a menu button (not shown) displayed viathe user interface 502. The user may also add, delete and/or repositionthe landmarks that anchor the non-linear mapping by selecting thecorresponding features in the images (402 e and 404 e). Otherconstraints may also be added, removed or updated via the user interface502. The system 100 then automatically updates the non-linear mappingwith the new constraints. This may be necessary if, for example, thecontour of the lungs is not matched accurately due to respiratorymotion, as shown in FIG. 6.

More particularly, FIG. 6 shows an exemplary user interface 502displaying first and second images (402 g and 404 g). In the first image402 g, the navigational cross-hair is located at the bottom location 506of the left lung 514. Due to respiratory motion, the size of the leftlung 514 in the second image 404 g is relatively smaller. As such, thecorresponding target cross-hair may be mapped to a wrong location 508inside the lung 514. To correct for this misalignment, the user may addadditional anchor landmarks located at 506 and 516 in the two images(402 g and 404 g) respectively, which correspond to the bottom of theleft lung 514.

Referring back to FIG. 5, the first image 402 e may be a two-dimensionalreference image and the second image 404 e may be a three-dimensionaltime series image. In such case, the cross-hair 504 in the second image404 e becomes a three-dimensional multi-time-point reference point. Inother words, the cross-hair 504 always correlates one point in the firstimage 402 e to a fixed target location in the second image 404 e acrossmultiple time points, irrespective of the particular view the first orsecond image may show. For example, as illustrated in FIG. 5, thecross-hair 516 in the coronal view image 402 e and the cross-hair 504 inthe axial view image 404 e map the same points in both windows to thesame anatomical feature.

While the present invention has been described in detail with referenceto exemplary embodiments, those skilled in the art will appreciate thatvarious modifications and substitutions can be made thereto withoutdeparting from the spirit and scope of the invention as set forth in theappended claims. For example, elements and/or features of differentexemplary embodiments may be combined with each other and/or substitutedfor each other within the scope of this disclosure and appended claims.

The invention claimed is:
 1. A method of synchronously navigatingimages, comprising: (i) receiving, by a computer system, at least firstand second medical images; (ii) generating, by the computer system, anon-linear mapping between the first and second medical images inresponse to addition, re-positioning or deletion of one or more anchorlandmarks that anchor the non-linear mapping; (iii) receiving, by thecomputer system, a selection of a given location in the first medicalimage in response to a user's navigational operation; (iv) applying, bythe computer system, the non-linear mapping to coordinates of only thegiven location, without deforming the second medical image, to determinecoordinates of a target location in the second medical image thatcorresponds to the given location in the first medical image; (v)generating, by the computer system and based at least in part on thetarget location, an optimized deformation-free view of the secondmedical image; and repeating steps (iii), (iv) and (v) while the userperforms navigational operations on the first medical image.
 2. Thecomputer-implemented method of claim 1 further comprising acquiring thefirst and second medical images using different imaging modalities. 3.The computer-implemented method of claim 1 further comprising acquiringthe first and second medical images at different times.
 4. Thecomputer-implemented method of claim 1, wherein generating thenon-linear mapping comprises performing a non-rigid registration.
 5. Thecomputer-implemented method of claim 1, wherein generating thenon-linear mapping comprises generating a rigid transformation and anon-linear interpolation.
 6. The computer-implemented method of claim 5,wherein generating the non-linear mapping comprises: identifying one ormore first landmarks in the first medical image; identifying one or morecorresponding second landmarks in the second medical image; anddetermining the rigid transformation and the non-linear interpolationbased on the first and second landmarks.
 7. The computer-implementedmethod of claim 6 further comprising providing a user interface toreceive user input of the first and second landmarks.
 8. Thecomputer-implemented method of claim 6 further comprising automaticallydetecting the first and second landmarks.
 9. The computer-implementedmethod of claim 5 wherein the non-linear interpolation comprisesB-spline interpolation.
 10. The computer-implemented method of claim 1further comprising providing a user interface to receive user input ofone or more constraints and automatically re-generating the non-linearmapping based on the one or more constraints.
 11. Thecomputer-implemented method of claim 10 wherein the one or moreconstraints comprise a type of interpolation method.
 12. Thecomputer-implemented method of claim 10 wherein the one or moreconstraints comprise an added, re-positioned or deleted anchor landmarkin the first or second medical image.
 13. The computer-implementedmethod of claim 1 wherein the user's navigational operation comprisesselecting the given location as a point of focus in the first medicalimage.
 14. The computer-implemented method of claim 1 wherein generatingthe optimized deformation-free view comprises displaying a target cursorat the target location in the second medical image.
 15. Thecomputer-implemented method of claim 1 wherein generating the optimizeddeformation-free view comprises panning the second medical image. 16.The computer-implemented method of claim 15 wherein palming the secondmedical image comprises automatically panning the second medical imagesuch that a target cursor at the target location follows a navigationalcursor at the given location to a same viewing window relative position.17. The computer-implemented method of claim 1 wherein generating theoptimized deformation-free view comprises generating a multi-planarreconstruction view of the second medical image.
 18. Thecomputer-implemented method of claim 1 wherein generating the optimizeddeformation-free view comprises generating a three-dimensional view ofthe second medical image.
 19. A non-transitory computer readable mediumembodying a program of instructions executable by machine to performsteps for synchronous image navigation, the steps comprising: (i)receiving at least first and second medical images; (ii) generating anon-linear mapping between the first and second medical images inresponse to addition, re-positioning or deletion of one or more anchorlandmarks that anchor the non-linear mapping; (iii) receiving aselection of a given location in the first medical image in response toa user's navigational operation; (iv) applying the non-linear mapping tocoordinates of only the given location, without deforming the secondmedical image, to determine coordinates of a target location in thesecond medical image that corresponds to the given location in the firstmedical image; (v) generating, based at least in part on the targetlocation, an optimized deformation-free view of the second medicalimage; and repeating steps (iii), (iv) and (v) while the user performsnavigational operations on the first medical image.
 20. A synchronousimage navigation system, comprising: a non-transitory memory device forstoring computer readable program code; and a processor in communicationwith the memory device, the processor being operative with the computerreadable program code to perform steps for synchronous navigation, thesteps comprising: (i) receiving at least first and second medicalimages; (ii) generating a non-linear mapping between the first andsecond medical images in response to addition, re-positioning ordeletion of one or more anchor landmarks that anchor the non-linearmapping; (iii) receiving a selection of a given location in the firstmedical image in response to a user's navigational operation; (iv)applying the non-linear mapping, without deforming the second medicalimage, to coordinates of only the given location to determinecoordinates of a target location in the second medical image thatcorresponds to the given location in the first medical image; (v)generating, based at least in part on the target location, an optimizeddeformation-free view of the second medical image; and repeating steps(iii), (iv) and (v) while the user performs navigational operations onthe first medical image.